If an individual connects, for example, his or her laptop connected to an alternating current (AC) supply, then the laptop draws power from the AC supply. This can occur irrespective of the amount of battery power available with the laptop. For a group of such individuals, if the charging of batteries and the source of power consumption is properly managed, the peak load (that is, the maximum AC power consumed at any time of the day) can be decreased. Similarly, if there are various distributed storage options in a facility and various load sources (AC sources, fans, lights, laptops, etc.), there can be a need for improved and/or efficient power management to reduce peak load.
Additionally, different entities may have different objectives but be related by some hierarchy (for instance, an IT park can include many companies, wherein each company has many individuals therein). Existing peak load reduction approaches in such a setting include a brute force method, implementing universal rules across the hierarchy. For example, such rules might include running each individual computer on battery power until the battery power is unavailable, at which point a switch is made to an AC power source (simultaneously recharging the battery). When the battery is fully charged, each individual computer returns to running on battery power, and this cycle is repeated.
Such a brute force method, however, does not optimize battery power usage, and problems can arise if one of the individual computers is unable to connect to the AC power source (for example, if a computer is unplugged for use in a meeting conducted at a location lacking an AC power source). Accordingly, such existing approaches can often fail to minimize the peak load in many settings.